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Taiwan-LLM_v3_tokenizer

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yentinglin commited on Mar 24, 2024

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 tags: []
 ---
+# Taiwan-LLM_v3_tokenizer
+This repository contains a custom tokenizer for the Taiwan-LLM v3 model, which is a Traditional Mandarin language model based on the LLaMA architecture. The tokenizer is created by merging a Mandarin SentencePiece model with the original LLaMA tokenizer, resulting in a vocabulary size of 64,000 tokens.
+## Features
+- Supports both English and Traditional Mandarin text tokenization
+- Includes special tokens `<|im_start|>` and `<|im_end|>`
+- Vocabulary size of 64,000 tokens
+- Compatible with the LLaMA/Mistral model architecture
+## Usage
+To use the Taiwan-LLM_v3_tokenizer in your project, you can install it using the following command:
+```bash
+pip install transformers
+```
+Then, you can load the tokenizer using the Hugging Face `LlamaTokenizer` class:
+```python
+from transformers import LlamaTokenizer
+taiwan_llm_tokenizer = LlamaTokenizer.from_pretrained("yentinglin/Taiwan-LLM_v3_tokenizer")
+original_llama_tokenizer = LlamaTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
+```
+Once the tokenizer is loaded, you can use it to tokenize both English and Traditional Chinese text:
+```python
+text_en = """During the recent GTC (GPU Technology Conference), Nvidia CEO Jensen Huang took time out of his busy schedule to dine with the Taiwanese community in Silicon Valley. In his speech at the gathering, Huang referred to himself as a "great ambassador for Taiwan," expressing his gratitude for the island nation's role in Nvidia's growth and success."""
+text_zh = "輝達（NVIDIA）執行長黃仁勳在GTC大會期間與矽谷台灣人餐敘，並在致詞時自詡為「很棒的台灣大使」。他說輝達和台灣一起成長，感謝台灣夥伴一路陪伴，「台灣拯救了輝達」。"
+taiwan_llm_tokens_en = taiwan_llm_tokenizer.tokenize(text_en)
+original_llama_tokens_en = original_llama_tokenizer.tokenize(text_en)
+taiwan_llm_tokens_zh = taiwan_llm_tokenizer.tokenize(text_zh)
+original_llama_tokens_zh = original_llama_tokenizer.tokenize(text_zh)
+print(f"English text:")
+print(f"Taiwan-LLM_v3_tokenizer: {len(taiwan_llm_tokens_en)} tokens")
+print(f"Original LLaMA tokenizer: {len(original_llama_tokens_en)} tokens")
+print(f"\nTraditional Chinese text:")
+print(f"Taiwan-LLM_v3_tokenizer: {len(taiwan_llm_tokens_zh)} tokens")
+print(f"Original LLaMA tokenizer: {len(original_llama_tokens_zh)} tokens")
+```
+## Training Data
+The Chinese SentencePiece model used in this tokenizer was trained on a diverse set of Traditional Mandarin text data, including:
+- Wikipedia articles
+- Legal documents
+- Online forum discussions
+- Cultural and historical texts
+This ensures that the tokenizer is well-suited for a wide range of Traditional Chinese language applications.
+## Tokenizer Merging Process
+The tokenizer was created by following these steps:
+1. Load and preprocess the Traditional Mandarin text data
+2. Train a Chinese SentencePiece model using the preprocessed text data
+3. Merge the Mandarin SentencePiece model with the LLaMA tokenizer
+## Acknowledgements
+This tokenizer was created using the LLaMA tokenizer and a custom-trained Mandarin SentencePiece model. We would like to thank the authors of the LLaMA model and the Hugging Face team for their contributions to the NLP community.